Inertial sensor aided heading and positioning for gnss vehicle navigation

ABSTRACT

An apparatus and method for providing an improved heading estimate of a mobile device in a vehicle is presented. First, the mobile device determines if it is mounted in a cradle attached to the vehicle; if so, inertia sensor data may be valid. While in a mounted stated, the mobile device determines whether it has been rotated in the cradle; if so, inertia sensor data may no longer be reliable and a recalibration to determine a new relative orientation between the vehicle and the mobile device is needed. If the mobile device is mounted and not recently rotated, heading data from multiple sensors (e.g., GPS, gyroscope, accelerometer) may be computed and combined to form the improved heading estimate. This improved heading estimate may be used to form an improved velocity estimate. The improved heading estimate may also be used to compute a bias to correct a gyroscope.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of and priority under 35 U.S.C.§119(e) to U.S. Provisional Application No. 61/440,730, filed Feb. 8,2011, and titled “Inertial Sensor Aided Heading and Positioning for GNSSVehicle Navigation”, which is incorporated herein by reference. Thisapplication also claims the benefit of and priority under 35 U.S.C.§119(e) to U.S. Provisional Application No. 61/433,124, filed Jan. 14,2011, and titled “Inertial Sensor Aided Heading and Positioning for GNSSVehicle Navigation”, which is incorporated herein by reference. Thisapplication further claims the benefit of and priority under 35 U.S.C.§119(e) to U.S. Provisional Application No. 61/419,786, filed Dec. 3,2010, and titled “Inertial Sensor Aided Heading and Positioning for GNSSVehicle Navigation”, which is incorporated herein by reference.

BACKGROUND

I. Field of the Invention

This disclosure relates generally to apparatus and methods fordetermining headings in a wireless device, and more particularly todetermining headings base on a combination of headings.

II. Background

Mobile devices used for navigation in a car or other vehicle sometimesuse Global Navigation Satellite Systems (GNSS) positioning informationin addition to accelerometer and gyroscope sensor measurements toformulate a current speed and heading. Such sensor measurements are mostvaluable when the mobile device “knows” how it is oriented with respectto the car. For example, the mobile device may be mounted in a cradle orother fixed structure in the car. Thus, when the mobile deviceexperiences motion, the car is usually experiencing the same motion. Onthe other hand, if the mobile device is in a shirt pocket or slidingaround on a seat or dashboard, then the mobile device motion and the carmotion are correlated but not identical.

To determine whether a mobile device is mounted in a cradle, the cradlemay have an electronic switch that is activated depending on whether themobile device is in a mounted state. Each of these known cradlesrequires an extra switch on the cradle and an extra interface on themobile device. Therefore, a means, without using extra cradle hardware,to detect whether a mobile device is mounted in a cradle is desired.Also, if a cradle provides pivoting or displacement with respect to thecar, the relative relationship between the mobile device and car willchange. Assuming that this relationship is permanently fixed while themobile device is in its cradle is invalid with such pivoting cradles.Therefore, a means to detect when a mounted mobile device has beenrepositioned is desired.

In addition, many mobile devices provide heading information to a userfor purposes of position location, navigation and routing. This headinginformation may be based on dead reckoning from sensor measurementsand/or GNSS information. Such sensors include accelerometers andgyroscopes. Methods have long been desired to better select, weight andcombine sensor measurements with GNSS information to provide a user witha more reliable and improved heading.

In addition to heading information, mobile devices include Kalmanfilters to predict a next GNSS position and velocity. The Kalman filtercan better predicted the position and velocity with error feedback.Methods to provide an improve error feedback signal have long beendesired.

Sensor calibration typically requires a mobile device to be stationaryor traveling at a constant velocity. Methods to calibrate sensors,including gyroscopes, that have no such restraints are also desired.

BRIEF SUMMARY

Disclosed is an apparatus and method for providing and using an accurateand improved heading for a mobile device in a vehicle. Before animproved heading estimate is determined, the mobile device determines ifthe mobile device is in a mounted state in a cradle attached to thevehicle; if so, then inertia sensor data may be valid. If already in amounted stated, the mobile device determines whether the mobile devicehas been rotated in the cradle; if so, then inertia sensor data may nolonger be reliable and a recalibration to determine a new relativeorientation between the vehicle and the mobile device is needed. If themounted state of the device is known such that the mobile device ismounted and not recently rotated, heading data from multiple sensors(e.g., GPS, gyroscope, accelerometer) may be computed and combined toform an improved heading estimate. This improved heading estimate may beused to form an improved velocity estimate. The improved headingestimate may also be used to compute a bias to correct gyroscopecalibration.

According to some aspects, disclosed is a method in a mobile device todetect if the mobile device is unmounted, the method comprising:receiving accelerometer measurements {right arrow over (a)}; determiningan average {right arrow over (a)}_(AVG) of the accelerometermeasurements {right arrow over (a)}; computing a parameter p based onthe accelerometer measurements {right arrow over (a)}; computing avariance σ_(p) ² of the parameter; comparing the variance σ_(p) ² to athreshold; and declaring the mobile device is unmounted based on thevariance σ_(p) ² being greater than the threshold.

According to some aspects, disclosed is a mobile device to detect if themobile device is unmounted, the mobile device comprising: an inertialmeasurement unit comprising an accelerometer; a processor coupled to theinertial measurement unit; and memory coupled to the processor,comprising code for: receiving accelerometer measurements {right arrowover (a)}; determining an average {right arrow over (a)}_(AVG) of theaccelerometer measurements {right arrow over (a)}; computing a parameterp based on the accelerometer measurements {right arrow over (a)};computing a variance σ_(p) ² of the parameter; comparing the varianceσ_(p) ² to a threshold; and declaring the mobile device is unmountedbased on the variance σ_(p) ² being greater than the threshold.

According to some aspects, disclosed is a mobile device to detect if themobile device is unmounted, the mobile device comprising: means forreceiving accelerometer measurements {right arrow over (a)}; means fordetermining an average {right arrow over (a)}_(AVG) of the accelerometermeasurements {right arrow over (a)}; means for computing a parameter pbased on the accelerometer measurements {right arrow over (a)}; meansfor computing a variance σ_(p) ² of the parameter; means for comparingthe variance σ_(p) ² to a threshold; and means for declaring the mobiledevice is unmounted based on the variance σ_(p) ² being greater than thethreshold.

According to some aspects, disclosed is a method in a mobile device todetect if the mobile device is unmounted, the method comprising:receiving gyroscope measurements {right arrow over (ω)}; determining anaverage {right arrow over (ω)}_(AVG) of the gyroscope measurements{right arrow over (ω)}; computing a parameter p based on the gyroscopemeasurement {right arrow over (ω)}; computing a variance σ_(p) ² of theparameter; comparing the variance σ_(p) ² to a threshold; and declaringthe mobile device is unmounted based on the variance σ_(p) ² beinggreater than the threshold.

According to some aspects, disclosed is a mobile device to detect if themobile device is unmounted, the mobile device comprising: an inertialmeasurement unit comprising a gyroscope; a processor coupled to theinertial measurement unit; and memory coupled to the processor,comprising code for: receiving gyroscope measurements {right arrow over(ω)}; determining an average {right arrow over (ω)}_(AVG) of thegyroscope measurements {right arrow over (ω)}; computing a parameter pbased on the gyroscope measurement {right arrow over (ω)}; computing avariance σ_(p) ² of the parameter; comparing the variance σ_(p) ² to athreshold; and declaring the mobile device is unmounted based on thevariance σ_(p) ² being greater than the threshold.

According to some aspects, disclosed is a mobile device to detect if themobile device is unmounted, the mobile device comprising: means forreceiving gyroscope measurements {right arrow over (ω)}; means fordetermining an average {right arrow over (ω)}_(AVG) of the gyroscopemeasurements {right arrow over (ω)}; means for computing a parameter pbased on the gyroscope measurement {right arrow over (ω)}; means forcomputing a variance σ_(p) ² of the parameter; means for comparing thevariance σ_(p) ² to a threshold; and means for declaring the mobiledevice is unmounted based on the variance σ_(p) ² being greater than thethreshold.

According to some aspects, disclosed is a method in a mobile device todetect if the mobile device is unmounted, the method comprising:receiving accelerometer measurements {right arrow over (a)}; determiningan average {right arrow over (a)}_(AVG) of the accelerometermeasurements {right arrow over (a)}; computing an angle θ between theaverage {right arrow over (a)}_(AVG) and an axis perpendicular to aviewable display on the mobile device; comparing the angle θ to 90degrees; and declaring the mobile device is unmounted based on the angleθ being less than a threshold from 90 degrees.

According to some aspects, disclosed is a mobile device to detect if themobile device is unmounted, the mobile device comprising: an inertialmeasurement unit comprising an accelerometer; a processor coupled to theinertial measurement unit; and memory coupled to the processor,comprising code for: receiving accelerometer measurements {right arrowover (a)}; determining an average {right arrow over (a)}_(AVG) of theaccelerometer measurements {right arrow over (a)}; computing an angle θbetween the average {right arrow over (a)}_(AVG) and an axisperpendicular to a viewable display on the mobile device; comparing theangle θ to 90 degrees; and declaring the mobile device is unmountedbased on the angle θ being less than a threshold from 90 degrees.

According to some aspects, disclosed is a mobile device to detect if themobile device is unmounted, the mobile device comprising: means forreceiving accelerometer measurements {right arrow over (a)}; means fordetermining an average {right arrow over (a)}_(AVG) of the accelerometermeasurements {right arrow over (a)}; means for computing an angle θbetween the average {right arrow over (a)}_(AVG) and an axisperpendicular to a viewable display on the mobile device; means forcomparing the angle θ to 90 degrees; and means for declaring the mobiledevice is unmounted based on the angle θ being less than a thresholdfrom 90 degrees.

According to some aspects, disclosed is a method in a mobile device todetect if the mobile device is unmounted, the method comprising:computing variance values σ_(p) ² for at least one of: a sequence ofaccelerometer measurements ({right arrow over (a)}); a sequence ofaverages ({right arrow over (a)}_(AVG) ) of accelerometer measurements;a sequence of gyroscope measurements ({right arrow over (a)}_(AVG));three sequences of scalar channelized gyroscope measurements (ω_(X),ω_(Y), ω_(Z)); and a virtual gyroscope heading rate ({dot over ( H);setting a flag if any of the variance values σ_(p) ² exceed a respectivethreshold; and declaring the mobile device is not in a mounted statebased on the flag.

According to some aspects, disclosed is a mobile device to detect if themobile device is unmounted, the mobile device comprising: an inertialmeasurement unit comprising an accelerometer; a processor coupled to theinertial measurement unit; and memory coupled to the processor,comprising code for: computing variance values σ_(p) ²for at least oneof: a sequence of accelerometer measurements ({right arrow over (a)}); asequence of averages ({right arrow over (a)}_(AVG)) of accelerometermeasurements; a sequence of gyroscope measurements ({right arrow over(ω)}); three sequences of scalar channelized gyroscope measurements(ω_(X), ω_(Y), ω_(Z)); and a virtual gyroscope heading rate ({dot over (H); setting a flag if any of the variance values σ_(p) ² exceed arespective threshold; and declaring the mobile device is not in amounted state based on the flag. A mobile device to detect if the mobiledevice is unmounted, the mobile device comprising: means for computingvariance values σ_(p) ² for at least one of: a sequence of accelerometermeasurements ({right arrow over (a)}); a sequence of averages ({rightarrow over (a)}_(AVG)) of accelerometer measurements; a sequence ofgyroscope measurements ({right arrow over (ω)}); three sequences ofscalar channelized gyroscope measurements (ω_(X), ω_(Y), ω_(Z)); and avirtual gyroscope heading rate ({dot over ( H); means for setting a flagif any of the variance values σ_(p) ² exceed a respective threshold; andmeans for declaring the mobile device is not in a mounted state based onthe flag.

According to some aspects, disclosed is a method in a mobile device forproviding an improved heading, the method comprising: receiving anaccelerometer measurement ({right arrow over (a)}) from anaccelerometer; receiving a gyroscope measurement ({right arrow over(ω)}) from a gyroscope; receiving a GNSS heading (H_(GNSS)) from a GNSSreceiver; computing a gravity vector ({right arrow over (g)}) based onthe accelerometer measurement ({right arrow over (a)}); computing avirtual gyroscope heading rate ({dot over (H)}_(Gyro)) based on thegyroscope measurement ({right arrow over (ω)}) and the gravity vector({right arrow over (g)}); and combining the GNSS heading (H_(GNSS)) andvirtual gyroscope heading rate ({dot over (H)}_(Gyro)) to form theimproved heading.

According to some aspects, disclosed is a mobile device to provide animproved heading, the mobile device comprising: an inertial measurementunit comprising an accelerometer and a gyroscope; a Global NavigationSatellite Systems receiver (GNSS receiver); a processor coupled to theinertial measurement unit and the GNSS receiver; and memory coupled tothe processor, comprising code for: receiving an accelerometermeasurement ({right arrow over (a)}) from the accelerometer; receiving agyroscope measurement ({right arrow over (ω)}) from the gyroscope;receiving a GNSS heading (H_(GNSS)) from the GNSS receiver; computing agravity vector ({right arrow over (g)}) based on the accelerometermeasurement ({right arrow over (a)}); computing a virtual gyroscopeheading rate ({dot over (H)}_(Gyro)) based on the gyroscope measurement({right arrow over (ω)}) and the gravity vector ({right arrow over(g)}); and combining the GNSS heading (H_(GNSS)) and virtual gyroscopeheading rate ({dot over (H)}_(Gyro)) to form the improved heading.

According to some aspects, disclosed is a mobile device to provide animproved heading, the mobile device comprising: means for receiving anaccelerometer measurement ({right arrow over (a)}); means for receivinga gyroscope measurement ({right arrow over (ω)}); means for receiving aGNSS heading (H_(GNSS)); means for computing a gravity vector ({rightarrow over (g)}) based on the accelerometer measurement ({right arrowover (a)}); means for computing a virtual gyroscope heading rate ({dotover (H)}_(Gyro)) based a projection of the gyroscope measurement({right arrow over (ω)}) and the gravity vector ({right arrow over(g)}); and means for combining the GNSS heading (H_(GNSS)) and virtualgyroscope heading rate

According to some aspects, disclosed is a method in a mobile device forproviding an improved velocity, the method comprising: receiving anaccelerometer measurement ({right arrow over (a)}) from anaccelerometer; receiving a gyroscope measurement ({right arrow over(ω)}) from a gyroscope; receiving a GNSS velocity ({right arrow over(V)}_(GNSS)) comprising a GNSS heading ({right arrow over (H)}_(GNSS))from a GNSS receiver; computing a gravity vector ({right arrow over(g)}) based on the accelerometer measurement ({right arrow over (a)});computing a virtual gyroscope heading rate ({dot over (H)}_(Gyro)) basedon a projection of the gyroscope measurement ({right arrow over (ω)})and the gravity vector ({right arrow over (g)}); combining the GNSSheading (H_(GNSS)) and virtual gyroscope heading rate ({dot over(H)}_(Gyro)) to form an improved heading (Ĥ); and computing the improvedvelocity from the improved heading (Ĥ).

According to some aspects, disclosed is a mobile device to provide animproved velocity, the mobile device comprising: an inertial measurementunit comprising an accelerometer and a gyroscope; a Global NavigationSatellite Systems receiver (GNSS receiver); a processor coupled to theinertial measurement unit and the GNSS receiver; and memory coupled tothe processor, comprising code for: receiving an accelerometermeasurement ({right arrow over (a)}) from the accelerometer; receiving agyroscope measurement ({right arrow over (ω)}) from the gyroscope;receiving a GNSS velocity ({right arrow over (V)}_(GNSS)) comprising aGNSS heading ({right arrow over (H)}_(GNSS)) from the GNSS receiver;computing a gravity vector ({right arrow over (g)}) based on theaccelerometer measurement ({right arrow over (a)}); computing a virtualgyroscope heading rate ({dot over (H)}_(Gyro)) based on the gyroscopemeasurement ({right arrow over (ω)}) and the gravity vector ({rightarrow over (g)}); combining the GNSS heading (H_(GNSS)) and virtualgyroscope heading rate ({dot over (H)}_(Gyro)) to form an improvedheading (Ĥ); and computing the improved velocity from the improvedheading (Ĥ).

According to some aspects, disclosed is a mobile device to provide animproved velocity, the mobile device comprising: means for receiving anaccelerometer measurement ({right arrow over (a)}) from anaccelerometer; means for receiving a gyroscope measurement ({right arrowover (ω)}) from a gyroscope; means for receiving a GNSS velocity ({rightarrow over (V)}_(GNSS)) comprising a GNSS heading ({right arrow over(H)}_(GNSS)) from a GNSS receiver; means for computing a gravity vector({right arrow over (g)}) based on the accelerometer measurement ({rightarrow over (a)}); means for computing a virtual gyroscope heading rate({dot over (H)}_(Gyro)) based on the gyroscope measurement ({right arrowover (ω)}) and the gravity vector ({right arrow over (g)}); means forcombining the GNSS heading (H_(GNSS)) and virtual gyroscope heading rate({dot over (H)}_(Gyro)) to form an improved heading (Ĥ); and means forcomputing the improved velocity from the improved heading (Ĥ).

According to some aspects, disclosed is a method in a mobile device forgenerating a gyroscope bias, the method comprising: receiving at leasttwo GNSS headings (H_(GNSS)) from a GNSS receiver; computing a GNSSheading difference (ΔH_(GNSS)) based on the GNSS headings (H_(GNSS));converting the GNSS heading difference (ΔH_(GNSS)) to a negative GNSSheading rate (−{dot over (H)}_(GNSS)) by scaling by a GNSS timedifference (−ΔT_(GNSS)); receiving gyroscope measurements ({right arrowover (ω)}) from a gyroscope; computing the virtual gyroscope headingrate ({dot over (H)}_(Gyro)) from the gyroscope measurements ({rightarrow over (ω)}); forming a corrected gyroscope heading rate ({dot over(H)}_(Gyro,CORR))from the virtual gyroscope heading rate ({dot over(H)}_(Gyro)); averaging the corrected gyroscope heading rate ({dot over(H)}_(Gyro,CORR)) to form the virtual gyroscope heading rate ({dot over( H _(Gyro,CORR)), and summing the negative GNSS heading rate (−{dotover (H)}_(GNSS)) and the virtual gyroscope heading rate ({dot over ( H_(Gyro)) to form the gyroscope bias.

According to some aspects, disclosed is a mobile device to generate agyroscope bias, the mobile device comprising: an inertial measurementunit comprising an accelerometer and a gyroscope; a Global NavigationSatellite Systems receiver (GNSS receiver); a processor coupled to theinertial measurement unit and the GNSS receiver; and memory coupled tothe processor, comprising code for: receiving GNSS headings (H_(GNSS))from the GNSS receiver; computing a GNSS heading difference (ΔH_(GNSS))based on the GNSS headings (H_(GNSS)); converting the GNSS headingdifference (ΔH_(GNSS)) to a negative GNSS heading rate (−{dot over(H)}_(GNSS)) by scaling by a GNSS time difference (−ΔT_(GNSS));receiving gyroscope measurements ({right arrow over (ω)}) from thegyroscope; computing the virtual gyroscope heading rate ({dot over(H)}_(Gyro)) from the gyroscope measurements ({right arrow over (ω)});forming a corrected gyroscope heading rate ({dot over (H)}_(Gyro,CORR))from the virtual gyroscope heading rate ({dot over (H)}_(Gyro));averaging the corrected gyroscope heading rate ({dot over(H)}_(Gyro,CORR)) to form the virtual gyroscope heading rate ({dot over( H _(Gyro,CORR)), and summing the average heading rate (−{dot over(H)}_(GNSS)) and the virtual gyroscope heading rate ({dot over ( H_(Gyro)) to form the gyroscope bias.

According to some aspects, disclosed is a mobile device to generate agyroscope bias, the mobile device comprising: means for receiving atleast two GNSS headings (H_(GNSS)) from a GNSS receiver; means forcomputing an GNSS heading difference (ΔH_(GNSS)) (based on the GNSSheadings (H_(GNSS)); means for converting the GNSS heading difference(ΔH_(GNSS)) to a negative GNSS heading rate (−{dot over (H)}_(GNSS)) byscaling by a GNSS time difference (−ΔT_(GNSS)); means for receivinggyroscope measurements ({right arrow over (ω)}) from a gyroscope; meansfor computing the virtual gyroscope heading rate ({dot over (H)}_(Gyro))from the gyroscope measurements ({right arrow over (ω)}); means forforming a corrected gyroscope heading rate ({dot over (H)}_(Gyro,CORR))from the virtual gyroscope heading rate ({dot over (H)}_(Gyro)); meansfor averaging the corrected gyroscope heading rate ({dot over(H)}_(Gyro,CORR)) to form the virtual gyroscope heading rate ({dot over( H _(Gyro,CORR)), and means for summing the negative GNSS heading rate(−{dot over (H)}_(GNSS)) and the virtual gyroscope heading rate ({dotover ( H _(Gyro)) to form the gyroscope bias.

According to some aspects, disclosed is a method in a mobile device forgenerating a gyroscope bias, the method comprising: receiving a GNSSheading (H_(GNSS)); receiving gyroscope measurements ({right arrow over(ω)}); and computing the gyroscope bias based on the GNSS heading(H_(GNSS)) and the gyroscope measurements ({right arrow over (ω)}).

According to some aspects, disclosed is a mobile device to generate agyroscope bias, the mobile device comprising: an inertial measurementunit comprising an accelerometer and a gyroscope; a Global NavigationSatellite Systems receiver (GNSS receiver); a processor coupled to theinertial measurement unit and the GNSS receiver; and memory coupled tothe processor, comprising code for: receiving a GNSS heading (H_(GNSS));receiving gyroscope measurements ({right arrow over (ω)}); and computingthe gyroscope bias based on the GNSS heading (H_(GNSS)) and thegyroscope measurements ({right arrow over (ω)}).

According to some aspects, disclosed is a mobile device to generate agyroscope bias, the mobile device comprising: means for receiving a GNSSheading (H_(GNSS)); means for receiving gyroscope measurements ({rightarrow over (ω)}); and means for computing the gyroscope bias based onthe GNSS heading (H_(GNSS)) and the gyroscope measurements ({right arrowover (ω)}).

According to some aspects, disclosed is a method in a mobile device fordetecting cradle rotation, the method comprising: determining thethreshold (ω_(max)) based on a GNSS magnitude (∥{right arrow over(V)}∥); comparing an angular rotation rate (∥{right arrow over (ω)}∥) toa threshold (ω_(max)); and determining a cradle rotation state based onthe comparison.

According to some aspects, disclosed is a mobile device to detect cradlerotation, the mobile device comprising: an inertial measurement unitcomprising an accelerometer and a gyroscope; a Global NavigationSatellite Systems receiver (GNSS receiver); a processor coupled to theinertial measurement unit and the GNSS receiver; and memory coupled tothe processor, comprising code for: determining the threshold (ω_(max))based on a GNSS magnitude (∥{right arrow over (V)}∥); comparing anangular rotation rate (∥{right arrow over (ω)}∥) to a threshold(ω_(max)); and determining a cradle rotation state based on thecomparison.

According to some aspects, disclosed is a mobile device to detect cradlerotation, the mobile device comprising: means for determining thethreshold (ω_(max)) based on a GNSS magnitude (∥{right arrow over(V)}∥); means for comparing an angular rotation rate (∥{right arrow over(ω)}∥) to a threshold (ω_(max)); and means for determining a cradlerotation state based on the comparison.

It is understood that other aspects will become readily apparent tothose skilled in the art from the following detailed description,wherein it is shown and described various aspects by way ofillustration. The drawings and detailed description are to be regardedas illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWING

Embodiments of the invention will be described, by way of example only,with reference to the drawings.

FIG. 1 illustrates a GNSS system and a side view of a car with a mountedmobile device.

FIG. 2 shows a top view illustrating differences among headings fromvarious sources.

FIG. 3 shows a block diagram of a mobile device, in accordance with someembodiments of the present invention.

FIG. 4 shows gravity vectors with respect to a mobile device, inaccordance with some embodiments of the present invention.

FIG. 5 is a block diagram of a mount state detector, in accordance withsome embodiments of the present invention.

FIG. 6 is a block diagram of a unit to compute a variance.

FIGS. 7-10 are block diagrams of other mount state detectors, inaccordance with some embodiments of the present invention.

FIGS. 11 and 12 show two orientations of a mobile device 100.

FIG. 13 shows a mount state detector, in accordance with someembodiments of the present invention.

FIGS. 14 and 15 show block diagrams of units to compute an improvedheading, in accordance with some embodiments of the present invention.

FIG. 16 illustrates a projection used in a virtual gyroscope, inaccordance with some embodiments of the present invention.

FIG. 17 is a block diagram of a heading filter, in accordance with someembodiments of the present invention.

FIGS. 18 and 19 show a relationship between a weighting factor and aduration of integration.

FIGS. 20, 21, 22 and 23 illustrate a method for computing an improvedvelocity, in accordance with some embodiments of the present invention.

FIG. 24 shows a method for generating a gyroscope bias, in accordancewith some embodiments of the present invention.

FIG. 25 is a block diagram of a cradle rotation detector, in accordancewith some embodiments of the present invention.

DETAILED DESCRIPTION

The detailed description set forth below in connection with the appendeddrawings is intended as a description of various aspects of the presentdisclosure and is not intended to represent the only aspects in whichthe present disclosure may be practiced. Each aspect described in thisdisclosure is provided merely as an example or illustration of thepresent disclosure, and should not necessarily be construed as preferredor advantageous over other aspects. The detailed description includesspecific details for the purpose of providing a thorough understandingof the present disclosure. However, it will be apparent to those skilledin the art that the present disclosure may be practiced without thesespecific details. In some instances, well-known structures and devicesare shown in block diagram form in order to avoid obscuring the conceptsof the present disclosure. Acronyms and other descriptive terminologymay be used merely for convenience and clarity and are not intended tolimit the scope of the disclosure.

Position determination techniques described herein may be implemented inconjunction with various wireless communication networks such as awireless wide area network (WWAN), a wireless local area network (WLAN),a wireless personal area network (WPAN), and so on. The term “network”and “system” are often used interchangeably. A WWAN may be a CodeDivision Multiple Access (CDMA) network, a Time Division Multiple Access(TDMA) network, a Frequency Division Multiple Access (FDMA) network, anOrthogonal Frequency Division Multiple Access (OFDMA) network, aSingle-Carrier Frequency Division Multiple Access (SC-FDMA) network,Long Term Evolution (LTE), and so on. A CDMA network may implement oneor more radio access technologies (RATs) such as cdma2000, Wideband-CDMA(W-CDMA), and so on. Cdma2000 includes IS-95, IS-2000, and IS-856standards. A TDMA network may implement Global System for MobileCommunications (GSM), Digital Advanced Mobile Phone System (D-AMPS), orsome other RAT. GSM and W-CDMA are described in documents from aconsortium named “3rd Generation Partnership Project” (3GPP). Cdma2000is described in documents from a consortium named “3rd GenerationPartnership Project 2” (3GPP2). 3GPP and 3GPP2 documents are publiclyavailable. A WLAN may be an IEEE 802.11x network, and a WPAN may be aBluetooth network, an IEEE 802.15x, or some other type of network. Thetechniques may also be implemented in conjunction with any combinationof WWAN, WLAN and/or WPAN.

A satellite positioning system (SPS) typically includes a system oftransmitters positioned to enable entities to determine their locationon or above the Earth based, at least in part, on signals received fromthe transmitters. Such a transmitter typically transmits a signal markedwith a repeating pseudo-random noise (PN) code of a set number of chipsand may be located on ground based control stations, user equipmentand/or space vehicles. In a particular example, such transmitters may belocated on Earth orbiting satellite vehicles (SVs). For example, a SV ina constellation of Global Navigation Satellite System (GNSS) such asGlobal Positioning System (GPS), Galileo, GLONASS or Compass maytransmit a signal marked with a PN code that is distinguishable from PNcodes transmitted by other SVs in the constellation (e.g., usingdifferent PN codes for each satellite as in GPS or using the same codeon different frequencies as in GLONASS). In accordance with certainaspects, the techniques presented herein are not restricted to globalsystems (e.g., GNSS) for SPS. For example, the techniques providedherein may be applied to or otherwise enabled for use in variousregional systems, such as, e.g., Quasi-Zenith Satellite System (QZSS)over Japan, Indian Regional Navigational Satellite System (IRNSS) overIndia, Beidou over China, etc., and/or various augmentation systems(e.g., an Satellite Based Augmentation System (SBAS)) that may beassociated with or otherwise enabled for use with one or more globaland/or regional navigation satellite systems. By way of example but notlimitation, an SBAS may include an augmentation system(s) that providesintegrity information, differential corrections, etc., such as, e.g.,Wide Area Augmentation System (WAAS), European Geostationary NavigationOverlay Service (EGNOS), Multi-functional Satellite Augmentation System(MSAS), GPS Aided Geo Augmented Navigation or GPS and Geo AugmentedNavigation system (GAGAN), and/or the like. Thus, as used herein an SPSmay include any combination of one or more global and/or regionalnavigation satellite systems and/or augmentation systems, and SPSsignals may include SPS, SPS-like, and/or other signals associated withsuch one or more SPS.

As used herein, a mobile device 100, sometimes referred to as a mobilestation (MS) or user equipment (UE), such as a cellular phone, mobilephone or other wireless communication device, personal communicationsystem (PCS) device, personal navigation device (PND), PersonalInformation Manager (PIM), Personal Digital Assistant (PDA), laptop orother suitable mobile device which is capable of receiving wirelesscommunication and/or navigation signals. The term “mobile station” isalso intended to include devices which communicate with a personalnavigation device (PND), such as by short-range wireless, infrared,wireline connection, or other connection—regardless of whether satellitesignal reception, assistance data reception, and/or position-relatedprocessing occurs at the device or at the PND. Also, “mobile station” isintended to include all devices, including wireless communicationdevices, computers, laptops, etc. which are capable of communicationwith a server, such as via the Internet, WiFi, or other network, andregardless of whether satellite signal reception, assistance datareception, and/or position-related processing occurs at the device, at aserver, or at another device associated with the network. Any operablecombination of the above are also considered a “mobile station.”

FIG. 1 illustrates a satellite positioning system (SPS) and a side viewof a car 10 with a mobile device 100 mounted in a cradle. A user mayplace a mobile device 100 in a cradle fixed to the car 10 or othervehicle to assist the user with navigation while traveling in the car10. The mobile device 100 acts as a hands-free device, may display localmaps and may present audio and visual routing information.

The mobile device 100 may use GNSS signals received from GNSS satellites99 in the satellite positioning system (SPS) as well as local sensordata to estimate a current position, velocity or heading of the car 10within a horizontal plane. This horizontal plane may be defined as theX-Y plane perpendicular to the gravity vector ({right arrow over (g)}),which forms the Z-axis ({circumflex over (z)}). The X-axis ({circumflexover (x)}) may be defined as the direction the car 10 is facing and theY-axis (ŷ) (not shown) may be the yaw or lateral direction perpendicularto both the forward direction ({circumflex over (x)}) and the gravityvector ({right arrow over (g)}) in the vertical direction ({circumflexover (z)}).

The X, Y, and Z-axes represent a reference system local ({circumflexover (x)}, ŷ, {circumflex over (z)}), which is local to the car 10. Theglobal reference system may be represented by longitude and latitude orby longitude, latitude and elevation. GNSS data is generated withrespect to the global reference system. A third reference system is thebody reference system, which is defined relative to the body of themobile device 100. Sensor data is generated in the body reference systemby sensors within the mobile device 100.

Motion of the car 10 may be restricted using non-holonomic constraintsin the local reference system. That is, the mobile device 100 may placebounds on the car's possible movement by restricting movement of the car10 along the X-axis.

FIG. 2 shows a top view illustrating differences among headings fromvarious sources. A first heading (car headingH_({circumflex over (x)})along {circumflex over (x)}) is defined by thedirection the car 10 is heading or facing. In other words, the firstheading is a car heading (H_({circumflex over (x)})) defined by theX-axis in the local reference system.

A second heading is a GNSS heading (H_(GNSS)) defined by signals from aGNSS receiver 140. The second heading may be extracted from the GNSSvelocity ({right arrow over (V)}_(GNSS)) which may be defined as aheading and a speed.

A third heading (H_(Gyro)) is from a gyroscope 130.

The various headings in an ideal world are be identical. In the realworld, however, these three headings are derived from different sourcesand over different periods; therefore, these three headings are oftensimilar but distinct from one another. A fourth heading is an improvedheading (Ĥ), which is a weighted combination of two or more of thesethree headings and is in the direction of an improved velocity({circumflex over (V)}) discussed in more detail below.

Each heading has both advantages and disadvantages. The car heading(H_({circumflex over (x)})) is simple to determine but may only becorrect for an instant or have an inherent bias. The GNSS heading(H_(GNSS)) is accurate over the long term but less inaccurate over theshort term. The gyroscope heading (H_(Gyro)) may be accurate over theshort term but inaccurate over the long term because of accumulatederror. Two or three of the headings may be restricted (e.g., usingnon-holonomic constraints) and combined to form the improved heading(Ĥ), which attempts to combine the advantages of the individual headingswhile masking their disadvantages.

FIG. 3 shows a block diagram of a mobile device, in accordance with someembodiments of the present invention. The mobile device 100 includessensors 110, also referred to as an inertial measurement unit (IMU). Inthis case, the sensors 110 include both an accelerometer 120 and agyroscope 130. Both the accelerometer 120 and the gyroscope 130 may bedevices generating three-dimensional measurements.

The mobile device 100 also includes a GNSS receiver 140, a processor 150and possible a display 160. The processor 150 receives data from theaccelerometer 120 in the form of three-dimensional accelerometermeasurements ({right arrow over (a)}) referenced to the body referencesystem. The processor 150 receives from the gyroscope 130 an angularrate ({right arrow over (ω)}), which identifies an angular change from aprevious measurement. The angular rate ({right arrow over (ω)}) is alsoa three-dimensional measurement referenced to the body reference system.

The GNSS receiver 140 provides a GNSS heading ( H), which may be ascalar value referenced to North (0°). The processor 150 may includememory containing the code to execute the methods described herein.Alternatively, this memory may be externally located from the processor150. The display 160 is coupled to the processor and may be used topresent map, routing information and directional information to theuser. The mobile device 100 may also include a speaker for audiocommands.

FIG. 4 shows gravity vectors with respect to a mobile device, inaccordance with some embodiments of the present invention. When a userenters a car 10, the user may mount the mobile device 100 to a cradle orother mounting structure fixed to the car 10, for example, within thefirst 20 seconds. The cradle provides a fixed relationship between thebody reference system of the mobile device 100 and the local referencesystem of the car 10. Normally, a relative orientation between themobile device 100 and the car 10 does not change over time while themobile device 100 is mounted in the cradle. The fixed relationship maybe disturbed by a user, for example, readjusting the mobile device 100to obtain a better viewing angle.

When the mobile device 100 is positioned in the cradle, theaccelerometer 120 and the gyroscope 130 generate data directlycorresponding to movement of the car 10. On the other hand, when themobile device 100 is in a user's pocket, jacket or purse, the sensors110 generate data only correlated to but not identical to movements ofthe car 10. The figure shows an example when a gravitational vector({right arrow over (g)}) changes between a first time (k) and a secondtime (k−1). If mounted, the computed gravitational vector only changeswithin an error threshold.

If the computed gravitational vector changes from a first gravitation({right arrow over (g)}(k−1)) to a second gravitation ({right arrow over(g)}(k)) greater than a threshold amount, the processor 150 may declarethe mobile device 100 is not in a mounted state (if this variationchanges continuously with time) or rotated within the cradle (if thisvariation settles on a first gravitation vector then settles on a secondgravitation vector). If the processor 150 determines the mobile device100 is not mounted, then measurements from the sensor 110 will not bedirectly associated with movements of the car 10

FIG. 5 is a block diagram of a mount state detector, in accordance withsome embodiments of the present invention. This process 200 determinestwo or more variances from difference sequences of variables. Theexample here shows five variances, however, some embodiments may onlyuse just two, three or four of these variances. For example, anembodiment may use only one acceleration-based variance (e.g., avariance of {right arrow over (a)} or {right arrow over (a)}_(AVG)) andone gyroscope-based variance (e.g., a variance of {right arrow over (ω)}or ω_(X), ω_(Y), ω_(Z)). Another embodiment may use only an averagedacceleration variance (e.g., a variance of {right arrow over (a)}_(AVG))and one gyroscope-based variance (e.g., a variance of {right arrow over(ω)}).

The sequence of variables include: (1) a three-dimensional accelerometermeasurement ({right arrow over (a)}); (2) an averaged three-dimensionalaccelerometer measurement ({right arrow over (a)}_(AVG)); (3) athree-dimensional gyroscope measurement ({right arrow over (ω)}); (4) athree-dimensional gyroscope measurement treated as three separatechannels providing three separate scalar values (ω_(X), ω_(Y), ω_(Z));and (5) a virtual heading rate ({dot over ( H). The first three streamseach result in one variance value per time index and the fourth streamresults in three variance values per time index.

At step 210, the processor 150 receives accelerometer measurements({right arrow over (a)}(k)) from the accelerometer 120, which may beprovided to step 220, step 240 and/or step 270 as measurements ({rightarrow over (a)}). Alternatively, the three-dimensional accelerometermeasurements ({right arrow over (a)}(k)) may be provided as one, two orthree scalar values (a_(X), a_(Y), a_(Z) or a_(X)(k), a_(Y)(k),a_(Z)(k)) where {right arrow over (a)}(k)=a_(X)(k){circumflex over(X)}+a_(Y)(k)Ŷ+a_(Z)(k){circumflex over (Z)} in the body referencesystem. The accelerometer measurements ({right arrow over (a)}) may berepresented as a temporal sequence a values:

{right arrow over (a)}(k), {right arrow over (a)}(k−1), {right arrowover (a)}(k−2), {right arrow over (a)}(k−3), {right arrow over(a)}(k−4), . . .

At step 220, the processor 150 performs an average of a window of nprevious measurements, if needed. For example:

${{\overset{->}{a}}_{AVG}(k)} = {\frac{1}{n}{\sum\limits_{i = 0}^{n - 1}{\overset{->}{a}\left( {k - i} \right)}}}$

The three-dimensional averaged accelerometer measurements ({right arrowover (a)}_(AVG)) may be represented as a temporal sequence a values:

{right arrow over (a)}_(AVG)(k), {right arrow over (a)}_(AVG)(k−1),{right arrow over (a)}_(AVG)(k−2), {right arrow over (a)}_(AVG)(k−3),{right arrow over (a)}_(AVG)(k−4), . . .

At step 230, the processor 150 receives gyroscope measurements ({rightarrow over (ω)}(k)) from the gyroscope 130, which may also be providedto step 220, step 240 and/or step 270, either as three-dimensionalgyroscope measurements ({right arrow over (ω)}) or as the three separatechannels (ω_(X), ω_(Y), ω_(Z)). The three-dimensional gyroscopemeasurements ({right arrow over (ω)}) may be represented in time as:

{right arrow over (ω)}(k), {right arrow over (ω)}(k−1), {right arrowover (ω)}(k−2 ), {right arrow over (ω)}(k−3), {right arrow over(ω)}(k−4), . . .

The three separate channels (ω_(X), ω_(Y), ω_(Z)) may be represented intime as:

ω_(X)(k), ω_(X)(k−1), ω_(X)(k−2), ω_(X)(k−3), ω_(X)(k−4), . . . ,

ω_(Y)(k), ω_(Y)(k−1), ω_(Y)(k−2), ω_(Y)(k−3), ω_(Y)(k−4), . . . and

ω_(Z)(k), ω_(Z)(k−1), ω_(Z)(k−2), ω_(Z)(k−3), ω_(Z)(k−4), . . .

At step 270, the magnitude portion of the input value is passed as anoutput value to step 240. The processor 150 computes a magnitude from aninput vector {right arrow over (v)}(k) or input scalar v(k) as an outputscalar ∥{right arrow over (v)}(k)∥ or |v(k)|. For streams of datacomprising vectors, the processor 150 computes a magnitude and producesa scalar v(k), where the input data is {right arrow over(v)}(k)=v_(x)(k){circumflex over (x)}+v_(Y)(k)ŷ+v_(z)(k){circumflex over(z)} (with respect to the body reference system) and the output data isv(k)=√{square root over (v_(X)(k)²+v_(Y)(k)²+v_(Z)(k)²)}{square rootover (v_(X)(k)²+v_(Y)(k)²+v_(Z)(k)²)}{square root over(v_(X)(k)²+v_(Y)(k)²+v_(Z)(k)²)} (also with respect to the bodyreference system). For streams of data comprising scalars, the scalarvalues (a_(X), a_(Y), a_(Z), ω_(X), ω_(Y), ω_(Z)) may undergo anabsolute value operation (not shown) where, for example,v(k)=|v_(X)(k)|, where the output data is |v_(X)(k)| and the inputscalar value is one or more of (a_(X), a_(Y), a_(Z), ω_(X), ω_(Y),ω_(Z)). Output data may also be low pass filtered.

At step 240, the processor 150 receives one, two, three, four or fivestreams of input variables and computes a variance (σ²), based on anaverage acceleration (from step 220) or an average heading (from step310), for each stream at each time k. The variances are computed asdescribed with reference to the FIG. 6 described below. This stepcomputes a sequence of output variances corresponding to each set ofinput variables. For example, the processor 150 represents: the sequenceof variances (σ_(a) ²) computed from the three-dimensional accelerometermeasurements ({right arrow over (a)}) as σ_(a) ²(k), σ_(a) ²(k−1), σ_(a)²(k−2), σ_(a) ²(k−3), σ_(a) ²(k−4), . . . ; the sequence of variances(σ_(a) ²) computed from the averaged three-dimensional accelerometermeasurements ({right arrow over (a)}) as σ_(a) ²(k), σ_(a) ²(k−1), σ_(a)²(k−2), σ_(a) ²(k−3), σ_(a) ²(k−4), . . . ; the sequence of variances(σ_(ω) ²) computed from the three-dimensional gyroscope measurements({right arrow over (ω)}) as σ_(ω) ²(k), σ_(ω) ²(k−1), σ_(ω) ²(k−2),σ_(ω) ²(k−3), σ_(ω) ²(k−4), . . . ; and the sequence of variances (σ_(ω)_(—) _(X) ², σ_(ω) _(—) _(Y) ², σ_(ω) _(—) _(Z) ²) computed from thethree separate channels (ω_(X), ω_(Y), ω_(Z)) as:

σ_(ω) _(—) _(X) ²(k), σ_(ω) _(—) _(X) ²(k−1), σ_(ω) _(—) _(X) ²(k−2),σ_(ω) _(—) _(X) ²(k−3), σ_(ω) _(—) _(X) ²(k−4), . . . ,

σ_(ω) _(—) _(Y) ²(k), σ_(ω) _(—) _(Y) ²(k−1), σ_(ω) _(—) _(Y) ²(k−2),σ_(ω) _(—) _(Y) ²(k−3), σ_(ω) _(—) _(Y) ²(k−4), . . . and

σ_(ω) _(—) _(Z) ²(k), σ_(ω) _(—) _(Z) ²(k−1), σ_(ω) _(—) _(Z) ²(k−2),σ_(ω) _(—) _(Z) ²(k−3), σ_(ω) _(—) _(Z) ²(k−4), . . . .

At step 250, each of the computed variances is compared to a respectivethreshold to determine if any excessive linear or angular accelerationoccurred. In some embodiments, a single variance exceeding the thresholdtriggers the processor 150 into declaring the mobile device 100 is notin a mounted state, as shown at step 260.

In other embodiments, a window of results is examined for each sequenceof variances to determine if enough excessive events have occurred todeclare the mobile device 100 is not in the mounted state. For example,if the variance of the acceleration measurements is greater than a fixedthreshold for more than a predetermined number of times within a windowof a predetermined length, then the processor 150 declares the mobiledevice 100 is not in the mounted state. This windowing may occur inparallel for each of the sequences of variances computed at step 240. Inthis case, any one of the sequence of variances exceeding its respectivethreshold by more than the predetermined number of times within thewindow can trigger the processor 150 to make the declaration of step 260that the mobile device 100 is not in a mounted state.

Alternatively, or in addition to, the processor 150 may declare themobile device 100 is in the mounted state or remains in a mounted state(not shown) based on variances not exceeding a respective threshold formore than a predetermined number of times over a window of apredetermined time. A hysteresis may be used to transition between themounted and unmounted states.

Alternatively, the yes/no output of step 250 may be windowed such that apredetermined threshold number of decisions within a previouspredetermined number of computations must be ‘yes’ in order for thedeclaration at step 260 to be made. For example, two or more out of theprevious five determinations must be ‘yes’ before the declaration atstep 260 is made to conclude the mobile device 100 is in a mountedstate.

If the mobile device 100 is mounted, then the processor 150 assumes thebody reference system is fixed with respect to the local referencesystem. In this case, the sensor measurements from sensor 110 in thebody reference system directly represent movement of the car 10 in thelocal reference system, as well as the mobile device 100. That is, whenprocessor 150 determines the mobile device 100 is mounted, the processor150 may directly relate the two reference systems. When the processor150 determines the mobile device 100 is not mounted, the processor 150may disassociate the two reference systems.

FIG. 6 is a block diagram of a variances unit, which computes a variance(σ_(v) ²). As described above, the processor 150 computes a variance atstep 240 for each separate stream of data. At step 220 (FIG. 5), theprocessor 150 computes an average (v_(AVG)(k, n)) over a window. Forexample:

${{v_{AVG}\left( {k,n} \right)} = {\frac{1}{n}{\sum\limits_{i = 0}^{n - 1}{v\left( {k - i} \right)}}}},$

where the window size is n samples and k is the current index tomeasurements from the accelerometer 120 or gyroscope 130.

At step 246, the processor 150 uses the vector value {right arrow over(v)}(k) or scalar value v(k) and/or the averaged value (v_(AVG)(k)) tocompute a variance (σ_(v) ²). The variance may be denoted as σ_(v)²=var{v(k)}. As a result, the processor 150 accepts one, two, three,four or five or more streams in parallel to produce as correspondingvariance (σ_(v) ²) for each stream.

FIGS. 7-10 are block diagrams of other mount state detectors, inaccordance with some embodiments of the present invention. In FIG. 7, anangle θ(k), which represents an approximate instantaneous angle relativeto gravity, is used to compute a variance

(shown as σ_(θ,a) ²) of a sequence of angles θ(k).

An accelerometer 120 provides accelerometer measurements {right arrowover (a)}(k) (from a 3-D accelerometer) or a_(x)(k), a_(y)(k), and/ora_(z)(k) (from a 1-D, 2-D or 3-D accelerometer). At step 210, theprocessor 150 receives the accelerometer measurements {right arrow over(a)}(k) from the accelerometer 120 and forwards a vector or one or morescalars for processing to step 220 and step 235. For example, theprocessor 150 may process one, two or three of the scalar components ofthe accelerometer measurements {right arrow over (a)}(k).

At step 220, the processor 150 computes a running or windowed average

$\left( {{e.g.},{{{\overset{->}{a}}_{AVG}\left( {k,n} \right)} = {\frac{1}{n}{\sum\limits_{i = 0}^{n - 1}{\overset{->}{a}\left( {k - i} \right)}}}}} \right),$

which represents a gravity vector {right arrow over (g)}. At step 235,the processor 150 computes a parameter p, which is an angle θ(k) betweenthe averaged vector represented by the gravity vector {right arrow over(g)} and the current accelerometer measurement {right arrow over(a)}(k). For example, the angle is computed as:

${\theta (k)} = {{\arccos\left( \frac{{{\overset{->}{a}}_{AVG}\left( {k,n} \right)} \cdot {\overset{->}{a}(k)}}{{{{\overset{->}{a}}_{AVG}\left( {k,n} \right)}} \cdot {{\overset{->}{a}(k)}}} \right)}.}$

Step 235 presents angle θ(k) to step 240. At step 240 and based on angleθ(k), the processor 150 computes a variance

, which represents a variance of the angle formed between theaccelerometer measurements {right arrow over (a)}(k) and vector {rightarrow over (g)}. At step 250, the processor 150 determines if thisvariance

, is greater than a threshold variance. If so, the mobile device 100 isprobably not in a mounting device and at step 260, the processor 150declares that the mobile device 100 is not in a mounted state.

In FIG. 8, an instantaneous magnitude (e.g., ∥{right arrow over (a)}(k)∥or ∥{right arrow over (a)}_(y)(k)∥) is compared to a magnitude of anapproximation of the gravity to form a variance σ_(∥a∥) ². First, theaccelerometer 120 provides accelerometer measurements {right arrow over(a)}(k) (from a 3-D accelerometer) or a_(x)(k), a_(y)(k) and/or a_(z)(k)(from a 1-D, 2-D or 3-D accelerometer). Next at step 210, the processor150 receives the accelerometer measurements {right arrow over (a)}(k)and forwards a vector or one or more scalars for processing to step 270.

At step 270, the processor 150 determines a magnitude ∥{right arrow over(g)}∥ of the gravity vector {right arrow over (g)}, and determines amagnitude ∥{right arrow over (a)}(k)∥ of the passed along accelerometermeasurement {right arrow over (a)}(k) then passes the magnitude resultsto step 220 and step 240.

At step 220, the processor 150 determines an average

$\left( {{e.g.},{{{\overset{->}{a}}_{AVG}\left( {k,n} \right)} = {\frac{1}{n}{\sum\limits_{i = 0}^{n - 1}{{\overset{->}{a}\left( {k - i} \right)}}}}}} \right),$

which represents a magnitude of the gravity vector {right arrow over(g)}. At step 240, the processor 150 computes a variance σ_(∥a∥) ² basedon the magnitude ∥{right arrow over (a)}(k)∥ and the magnitude ∥{rightarrow over (g)}∥. The resulting variance σ_(|a|) ² represents a varianceof the magnitude formed between the accelerometer measurements {rightarrow over (a)}(k) and gravity vector {right arrow over (g)}.

At step 250, the processor 150 determines if this variance σ_(∥a∥) ² isgreater than a threshold variance. If so, the mobile device 100 isprobably not in a mounting device, and at step 260, the processor 150declares that mobile device 100 is not in a mounted state.

In FIG. 9, an angle θ(k) is shown, which represents an approximateinstantaneous angle relative to a vehicle's rotation, is used to computea variance of

of a sequence of angles θ(k). The gyroscope 130 provides gyroscopemeasurements {right arrow over (ω)}(k) (from a 3-D gyroscope) orω_(x)(k), ω_(y)(k) and/or ω_(z)(k) (from a 1-D, 2-D or 3-D gyroscope) tostep 230. At step 230, the processor 150 receives the gyroscopemeasurements {right arrow over (ω)}(k) and forwards a vector or one ormore scalars for processing. For example, the processor 150 may onlycollect one or two of the scalar components of the gyroscopemeasurements {right arrow over (ω)}(k).

At step 220, the processor 150 computes a running or windowed average

$\left( {{e.g.},{{{\overset{->}{\omega}}_{AVG}\left( {k,n} \right)} = {\frac{1}{n}{\sum\limits_{i = 0}^{n - 1}{\overset{->}{\omega}\left( {k - i} \right)}}}}} \right),$

which represents a turn rate or heading rate {dot over ( H of a vehicle.Alternatively, the method at step 310 (FIG. 14) described below may beused to form a heading rate {dot over ( H. At step 235, the processor150 computes a parameter p, which is an angle θ(k) between the averagedvector (e.g., heading rate {dot over ( H) from step 220 and the currentgyroscope measurement {right arrow over (ω)}(k) from step 230. Forexample:

${\theta (k)} = {{\arccos\left( \frac{{{\overset{->}{\omega}}_{AVG}\left( {k,n} \right)} \cdot {\overset{->}{\omega}(k)}}{{{{\overset{->}{\omega}}_{AVG}\left( {k,n} \right)}} \cdot {{\overset{->}{\omega}(k)}}} \right)}.}$

Based on the angle θ(k), at step 240, the processor 150 computes avariance

, which represents a variance of the angle formed between the gyroscopemeasurements {right arrow over (ω)}(k) and vehicle's heading rate {dotover ( H. At step 250, the processor 150 determines if this variance

is greater than a threshold variance. If so, the mobile device 100 isprobably not in a mounting device and at step 260, the processor 150declares that the mobile device 100 is not in a mounted state.

In FIG. 10, an instantaneous magnitude (e.g., ∥{right arrow over(ω)}(k)∥) is compared to a magnitude of an approximation of thevehicle's turn rate to form a variance σ_(∥{right arrow over (ω)}∥) ².First, the gyroscope 130 provides gyroscope measurements {right arrowover (ω)}(k) (from a 3-D gyroscope) or ω_(x)(k), ω_(y)(k) or ω_(z)(k)(from a 1-D, 2-D or 3-D gyroscope) to step 230. At step 230, theprocessor 150 receives the gyroscope measurements {right arrow over(ω)}(k) and forwards a vector or one or more scalars for processing.

At step 270, the processor 150 determines a magnitude of the rotationvector ∥{right arrow over (ω)}(k)∥ from the passed along gyroscopemeasurement {right arrow over (ω)}(k) then passes the results to step220 and step 240.

As described above, at step 220, the processor 150 determines an average

$\left( {{e.g.},{{{\overset{->}{\omega}}_{AVG}\left( {k,n} \right)} = {\frac{1}{n}{\sum\limits_{i = 0}^{n - 1}{\overset{->}{\omega}\left( {k - i} \right)}}}}} \right),$

which represents a heading rate {dot over ( H. Also at step 270, theprocessor 150 determines a magnitude ∥{dot over ( H∥ of the heading ratevector.

At step 240, the processor 150 computes a variance σ_(∥ω∥) ², whichrepresents a variance of the magnitude formed between the gyroscopemeasurements {right arrow over (ω)}(k) and the heading rate {dot over (Hof the vehicle.

At step 250, the processor 150 determines if this variance σ_(∥ω∥) ² isgreater than a threshold variance. If so, the mobile device 100 isprobably not in a mounting device and at step 260, the processor 150declares that mobile device 100 is not in a mounted state.

FIGS. 11 and 12 show two orientations of a mobile device 100. The mobiledevice 100 in a first orientation is probably viewable (thus, possiblyin a mounting apparatus) and in a second orientation is probablynon-viewable (thus, probably not in a mounting apparatus).

In FIG. 11, a mobile device 100 is shown in a relatively uprightposition (e.g., an orientation of the mobile device 100 while it ismounted). A body reference system is shown where a Z-axis ({circumflexover (z)}) protrudes perpendicular to the display 160, an X-axis({circumflex over (x)}) points to the left of the mobile device 100, anda Y-axis (ŷ) points up.

Also shown is a gravity axis ({right arrow over (g)}), which isrelatively perpendicular (within a threshold angle) to the Z-axis({circumflex over (z)}). If the angle θ is perpendicular to and within athreshold of the Z-axis ({circumflex over (z)}), then the mobile device100 is probably standing upright (as shown) or is standing horizontally(with its face horizontal). Thus, a processor 150 may declare the mobiledevice 100 is in a mounted state when the gravity axis ({right arrowover (g)}) is relatively perpendicular (within a threshold angle) to theZ-axis ({circumflex over (z)}).

In FIG. 12, a mobile device 100 is shown in a relatively flat position(e.g., an orientation of the mobile device 100 while it is on a carseat). In this case, the gravity axis ({right arrow over (g)}) isrelatively parallel (within a threshold angle) to the Z-axis({circumflex over (z)}). If the angle θ is parallel, within a threshold,to the Z-axis ({circumflex over (z)}), then the mobile device 100 isprobably lying flat. Thus, a processor 150 may declare the mobile device100 is in a non-mounted state.

FIG. 13 shows a mount state detector 200, in accordance with someembodiments of the present invention. The mount state detector mayaccept two or more input values from the variance

(from FIG. 7 and shown as σ_(θ,a) ²), the 200 variance σ_(∥a∥) ² (fromFIG. 8), the variance

(from FIG. 9 and shown as σ_(θ,ω) ²) the variance σ_(∥ω∥) ² (from FIG.10), and the viewable orientation θ (from FIGS. 11 and 12).

At step 255, the processor 150 declares if the mobile device 100 is notin a mounted state. The processor 150 may declare the mobile is not in amounted state if: (1) any one of the two or more input values declaresthe mobile device 100 is not in a mounted state; (2) a majority of thetwo or more input values declares the mobile device 100 is not in amounted state; (3) all of the two or more input values declares themobile device 100 is not in a mounted state; (4) a weighted combinationof decisions from the two or more input values (e.g., based on detectionaccuracy) is greater than a threshold (e.g., 0.5); or (5) a weightedcombination of the raw input values (e.g., based on detection accuracy)is greater than a threshold (e.g., 0.5).

FIGS. 14 and 15 show block diagrams of units to compute an improvedheading, in accordance with some embodiments of the present invention.

In FIG. 14, the process provides a heading rate based on theaccelerometer 123 and the gyroscope 130. As described above, theaccelerometer 120 produces acceleration measurements ({right arrow over(a)}) and the gyroscope 130 produces angular rate measurements ({rightarrow over (ω)}).

At step 305, the processor 150 computes a gravity vector ({right arrowover (g)}) based on a low pass filter of accelerometer measurements({right arrow over (a)}). If the mobile device 100 is fixed in a mountand the car 10 is either stationary or moving at a constant velocity,the gravity vector stays constant or settles in on a constant value.

At step 310, the processor 150 computes a virtual gyroscope heading rate({dot over (H)}_(Gyro)), which is based on the gravity vector ({rightarrow over (g)}) and the angular rate measurements ({right arrow over(ω)}) from the gyroscope 130. The virtual gyroscope heading rate ({dotover (H)}_(Gyro)) is described in additional detail with reference toFIG. 16.

In FIG. 15, the process 300 combines various headings to compute theimproved heading (Ĥ). As described above, the processor 150 produces avirtual gyroscope heading rate ({dot over (H)}_(Gyro)) usingmeasurements from the accelerometer 120 and the gyroscope 130. The GNSSreceiver 140 produces a GNSS heading (H_(GNSS)) and a time ΔT_(GNSS)between GNSS headings.

At step 320, a heading filter takes as input variables both the GNSSheading (H_(GNSS)) and the virtual gyroscope heading rate ({dot over(H)}_(GNSS)) to compute an improved heading (Ĥ). Step 320 is describedbelow with reference to FIG. 17 in further detail.

FIG. 16 illustrates a projection used in a virtual gyroscope, inaccordance with some embodiments of the present invention. At step 310described above with reference to FIG. 14, the processor 150 convertsthe raw three-dimensional gyroscope measurements ({right arrow over(ω)}) to a one-dimensional virtual gyroscope heading rate ({dot over(H)}_(Gyro)), which is parallel to the gravity vector ({right arrow over(g)}) and represents a rate of change in yaw in the horizontal plane.The processor 150 computes the virtual gyroscope heading rate ({dot over(H)}_(Gyro)) by projecting the gyroscope measurements ({right arrow over(ω)}) onto the gravity vector. In other words, {dot over(H)}_(Gyro)=−{right arrow over (ω)}_(⊥{right arrow over (g)})(k) where θdefines the angle between the two vectors ({right arrow over (ω)},{right arrow over (g)}).

The figure shows a gyroscope measurement ({right arrow over (ω)})defining a first plane perpendicular to the gyroscope measurement({right arrow over (ω)}). The figure also shows a gravity vector ({rightarrow over (g)}) defining a second plane perpendicular to the gravityvector ({right arrow over (g)}). The gravity vector ({right arrow over(g)}) is derived from the accelerometer measurements ({right arrow over(ω)}). The projection of the gyroscope measurement ({right arrow over(ω)}) onto the gravity vector ({right arrow over (g)}) results in thevirtual gyroscope heading rate ({dot over (H)}_(Gyro)) shownperpendicular to the second plane. The projection is defined as

${\overset{.}{H}}_{Gyro} = {{- {{\overset{->}{\omega}}_{\bot\overset{->}{g}}(k)}} = {{{- \frac{\overset{->}{g} \cdot \overset{->}{\omega}}{\overset{->}{g}}} \cdot \frac{\overset{->}{g}}{\overset{->}{g}}} = {{- {\overset{->}{\omega}}}{\cos (\theta)}{\frac{\overset{->}{g}}{\overset{->}{g}}.}}}}$

FIG. 17 is a block diagram of a heading filter, in accordance with someembodiments of the present invention. The heading filter 320, firstdescribed with reference to FIG. 15, receives the GNSS heading(H_(GNSS)(k)) and the virtual gyroscope heading rate ({dot over(H)}_(Gyro)(k)), processes and combines these two values, and thenproduces an improved heading (Ĥ(k). The improved heading (Ĥ(k)) isformed by a weighted combination of headings derived from the GNSSreceiver 140 and the gyroscope 130 as shown in FIGS. 14 and 15.

Mathematically, the improved heading (Ĥ(k)) may be represented by theformula:

Ĥ(k)=w·H _(GNSS)(k)+(1−w)[Ĥ(k−1)+ΔT _(GNSS)·{dot over (H)}_(Gyro)(k)]

where the weighting w may be represented by:

$w = \frac{\left( {\sigma_{\hat{H}{(k)}}^{2} + {\Delta \; {T_{GNSS}^{2} \cdot \sigma_{{\overset{.}{H}}_{Gyro}{(k)}}^{2}}}} \right)}{\left( {\sigma_{H_{GNSS}{(k)}}^{2} + \sigma_{\hat{H}{(k)}}^{2} + {\Delta \; {T_{GNSS}^{2} \cdot \sigma_{{\overset{.}{H}}_{Gryo}{(k)}}^{2}}}} \right)}$

where σ_(Ĥ(k)) ² is the variance of the improved heading (Ĥ(k)), whereσ_({dot over (H)}) _(Gyro) _((k)) ² is the variance of the virtualgyroscope heading rate ({dot over (H)}_(Gyro)(k)) from step 310 derivedfrom measurements from the gyroscope 130, where σ_(H) _(GNSS) _((k)) ²is the variance of the GNSS heading (H_(GNSS)(k)), and where ΔT_(GNSS)is the time separation between heading values from the GNSS receiver140.

The heading filter 320 comprises a first amplifier 321 that weights theGNSS heading (H_(GNSS)(k)) by a weighing value (w) and feeds this firstweighted heading w·H_(GNSS)(k)) to a first input of a first summer 322,which produces the improved heading (Ĥ(k)). A second input of the firstsummer 322 is provided by a chain that begins with scaling the virtualheading rate ({dot over (H)}_(Gyro)(k)) by the time (ΔT_(GNSS), whichrepresents a time between heading samples) with product operator 323.The product is feds to a second summer 324 that is also fed by a delayedversion (Ĥ(k−1)) of the improved heading (Ĥ(k)) from delay unit 325labeled with z⁻¹. The resulting sum is weighted by a second amplifier326 by a weighing value (1−w), which feeds the second input of the firstsummer 322. Typically, both weighting values (w and 1−w) range somewherebetween zero and one and sum to one.

FIGS. 18 and 19 show a relationship between a weighting factor and aduration of integration. FIG. 18 shows an expected relationship betweenan integration time and an expected weighting factor. GNSS-based headingvalues (e.g., H_(GNSS)(k)) taken over a short duration have lesscertainty (or equivalently, more uncertainty) than GNSS-based headingvalues taken over a long duration because of long-term trends.Therefore, GNSS-based heading values are typically given a higherweighting if the duration is longer.

In FIG. 19, an example inverse relationship is shown between integrationtime and an expected weighing factor. Gyro-based heading values (e.g.,from {dot over (H)}_(Gyro)(k)) taken over a short duration have morecertainty (or equivalently, less uncertainty) than gyro-based headingvalues taken over a long duration because of accumulated error.Therefore, gyro-based heading values are typically given a higherweighting if the duration is shorter.

FIGS. 20, 21, 22 and 23 illustrate a method for computing an improvedvelocity, in accordance with some embodiments of the present invention.In FIG. 20, a top view of a car 10 is shown. The car 10 travels along afirst direction indicated as along the X-axis ({circumflex over (x)}) inthe local reference system. The improved heading (Ĥ), which is afunction of the GNSS heading (H_(GNSS)) and the virtual gyroscopeheading rate) ({dot over (H)}_(Gyro)), often faces in a directiondifferent than X-axis. The improved velocity ({right arrow over(V)}_(//Ĥ)) is parallel to or collinear with the improved heading (Ĥ).

Similarly, the Kalman-filtered GNSS velocity ({right arrow over(V)}_(GNSS)), is parallel to or collinear with the Kalman-filtered GNSSheading ({right arrow over (H)}_(GNSS)) and is derived from the GNSSheading (H_(GNSS)). The improved velocity ({right arrow over (V)}_(//Ĥ))has a direction component and a magnitude component. The direction ofthe improved velocity ({right arrow over (V)}_(//Ĥ)) may be formed byadopting the direction of the improved heading (Ĥ). The magnitude of theimproved velocity) ({right arrow over (V)}_(//Ĥ)) may be formed from aprojection of the Kalman-filtered GNSS velocity ({right arrow over(V)}_(GNSS)) on the improved heading (Ĥ), thus

${\overset{->}{V}}_{//\hat{H}} = {\frac{{\overset{->}{V}}_{GNSS}}{\cos \; (\theta)}\angle \; {{ang}\left( \hat{H} \right)}}$

where θ defines the angle formed between {right arrow over (V)}_(GNSS)and Ĥ, and ∠ang (Ĥ) is the angular direction of Ĥ.

In FIG. 21, another top view of a car 10 is shown similar to thatdescribed above and shown in FIG. 20 except the magnitude of theimproved velocity ({right arrow over (V)}_(//Ĥ)) is calculated in analternative fashion. As before, the improved velocity ({right arrow over(V)}_(//Ĥ)) includes a direction component formed by adopting thedirection of the improved heading (Ĥ). In this alternative embodiment,the magnitude of the improved velocity ({right arrow over (V)}_(//Ĥ))may be formed by keeping the magnitude of the Kalman-filtered GNSSvelocity ({right arrow over (V)}_(GNSS)), thus {right arrow over(V)}_(//Ĥ)=∥{right arrow over (V)}_(GNSS)∥∠ang(Ĥ) where ∥{right arrowover (V)}_(GNSS)∥ is the magnitude of {right arrow over (V)}_(GNSS).

In comparing the methods of FIGS. 20 and 21, the former forms theimproved velocity ({right arrow over (V)}_(//Ĥ)) using a formalmathematical projection on the improved heading (Ĥ), whereas the latterforms the improved velocity ({right arrow over (V)}_(//Ĥ)) by aninformal projection (rotation) on the improved heading (Ĥ). Both ofthese methods are presented as projecting the Kalman-filtered GNSSvelocity ({right arrow over (V)}_(GNSS)) on the improved heading (Ĥ).The methods described above utilized the Kalman-filtered GNSS velocity({right arrow over (V)}_(GNSS)). Alternative methods use a velocity({right arrow over (V)}_(GNSS-based)) based on the GNSS heading ({rightarrow over (H)}_(GNSS)), where the Kalman-filtered GNSS velocity ({rightarrow over (V)}_(GNSS)) is an example of the GNSS based velocity ({rightarrow over (V)}_(GNSS-based)).

The difference between the improved velocity ({right arrow over(V)}_(//Ĥ)) and the Kalman filtered GNSS velocity ({right arrow over(V)}_(GNSS)) (or generally, the GNSS based velocity ({right arrow over(V)}_(GNSS-based))) forms a velocity error (Δ{right arrow over (V)})which is fed back to the Kalman filter 410 as a correction signal. Inthis manner, the velocity error (Δ{right arrow over (V)}) is used by theKalman filter 410 to improve future velocity estimates as describedbelow.

In FIG. 22, the block diagram shows steps a processor 150 executes togenerate an improved velocity ({right arrow over (V)}_(//Ĥ)) and avelocity error (Δ{right arrow over (V)}) At step 410, the processor 150receives a GNSS velocity ({right arrow over (V)}_(GNSS)) from the GNSSreceiver 140 and passes the GNSS velocity ({right arrow over(V)}_(GNSS)) through a Kalman filter to produce a Kalman-filtered GNSSvelocity ({right arrow over (V)}_(GNSS)).

At step 420, the processor 150 projects the Kalman-filtered GNSSvelocity ({right arrow over (V)}_(GNSS)) against the improved heading(Ĥ) to compute an improved velocity ({right arrow over (V)}_(//Ĥ)),which is parallel to the improved heading (Ĥ). Step 420 also computesthe difference between the Kalman-filtered GNSS velocity ({right arrowover (V)}_(GNSS)) and the improved velocity vector ({right arrow over(V)}_(//Ĥ)) to generate the velocity error (Δ{right arrow over (V)}). Asdescribed above, the velocity error (Δ{right arrow over (V)}) is used bythe Kalman filter 410 as a correction value to improve the nextKalman-filtered GNSS velocity ({right arrow over (V)}_(GNSS)). Inessence, the velocity error (Δ{right arrow over (V)}) is determinedusing a non-holonomic constraint and is used to correct Kalman filter.

In FIG. 23, the block diagram shows another set of steps a processor 150executes to generate an improved velocity ({right arrow over (V)}_(//Ĥ))and a velocity error (Δ{right arrow over (V)}). At step 415, theprocessor 150 receives a GNSS velocity ({right arrow over (V)}_(GNSS))from the GNSS receiver 140 and passes the GNSS velocity ({right arrowover (V)}_(GNSS)) through a position/velocity (Δ{right arrow over (V)})module to produce a PV filtered GNSS velocity ({right arrow over(V)}_(GNSS)).

Again, at step 420, the processor 150 projects the PV filtered GNSSvelocity ({right arrow over (V)}_(GNSS)) against the improved heading(Ĥ) to compute an improved velocity ({right arrow over (V)}_(//Ĥ)),which is parallel to the improved heading (Ĥ). Again, step 420 computesthe difference between the PV filtered GNSS velocity ({right arrow over(V)}_(GNSS)) and the improved velocity vector ({right arrow over(V)}_(//Ĥ)) to generate the velocity error (Δ{right arrow over (V)}).The velocity error (Δ{right arrow over (V)}) is used by the PV module415 as a correction value to improve the next PV filtered GNSS velocity({right arrow over (V)}_(GNSS)). The velocity error (Δ{right arrow over(V)}) is determined using a non-holonomic constraint and is used tocorrect Kalman filter.

FIG. 24 shows a method for generating a gyroscope bias, in accordancewith some embodiments of the present invention. Mathematically, thegyroscope bias may be represented as:

${GyroscopeBias} = {{{\overset{\overset{\_}{.}}{H}}_{{Gyro},{CORR}}(k)} - \frac{\left\lbrack {\Delta \; {H_{GNSS}(k)}} \right\rbrack}{\Delta \; T_{GNSS}}}$where  [Δ H_(GNSS)(k)] = H_(GNSS)(k) − H_(GNSS)(k − n + 1)  and${{where}\mspace{14mu} {{\overset{.}{H}}_{{Gyro},{CORR}}(k)}} = {\frac{1}{n}{\sum\limits_{i = 0}^{n - 1}{{{\overset{.}{H}}_{Gyro}\left( {k - i} \right)}.}}}$

At step 510, the processor 150 receives a GNSS heading (H(k)) from theGNSS receiver 140 and computes a GNSS heading change over a window([ΔH_(GNSS)(k)]). At step 520, the processor 150 converts the GNSSheading change (ΔH_(GNSS)) from a heading to a rate by scaling ordividing by a negative of the GNSS time difference (−ΔT_(GNSS)) to forma GNSS heading rate (− H _(GNSS)), which is fed as a first input to afirst summer 530. A second input of the first summer 530 is fed anaverage of the virtual gyroscope heading rate ({dot over(H)}_(Gyro,CORR)(k) and the sum of these two inputs (or equivalently, adifferences between the two average rates) forms the gyroscope biassignal (GyroscopeBias).

The processor 150 forms the average gyroscope heading rate ({dot over(H)}_(Gyro,CORR)(k)) from a chain beginning at step 310 of FIG. 14,which receives the gyroscopic measurements ({right arrow over (ω)}) andcomputes a virtual gyroscope heading rate ({dot over (H)}_(Gyro)) asdescribed above. The virtual gyroscope heading rate ({dot over(H)}_(Gyro)) is fed to a second summer 540 as a first input. A secondinput to the second summer 540 may be feedback as a preliminarygyroscope bias, which may be set by the previously determined gyroscopebias, where Gyroscope Correction=−GyroscopeBias(error). The secondsummer 540 produces a corrected gyroscope heading rate ({dot over(H)}_(Gyro,CORR)(k)={dot over (H)}_(Gyro)+GyroscopeBias). The processor150 averages the corrected gyroscope heading rate ({dot over(H)}_(Gyro,CORR)(k) to form the average gyroscope heading rate ({dotover ( H _(Gyro,CORR)(k)). A new gyroscope bias may be computed at eachiteration k or after a predetermined number of iterations.

FIG. 25 is a block diagram of a cradle rotation detector, in accordancewith some embodiments of the present invention. The process 600 beginsat step 610 where the process 150 converts the Kalman-filtered GNSSvelocity ({right arrow over (V)}_(GNSS)) to a magnitude (∥{right arrowover (V)}∥={right arrow over (V)}_(GNSS)∥).

At step 620, the processor 150 compares the magnitude (∥{right arrowover (V)}∥) to a velocity threshold to show if the mobile device 100 isrelatively still in its cradle or being rotated within the cradle by theuser. Depending on the level of relative movement, a second threshold(ω_(max)) is set. When the processor 150 determines the mobile device100 is still or moving below the threshold (at a value less than themagnitude threshold) with respect to the cradle, the second threshold(ω_(max)) is set based on a division of the magnitude (∥{right arrowover (V)}∥) and the minimum radius of curvature (R_(min)) expected of acar 10 as shown at step 630.

When the processor 150 determines the mobile device 100 is moving orrotating (at a value greater than the magnitude threshold) relative tothe cradle, the second threshold (ω_(max)) is set based on a division ofthe maximum acceleration (a_(max)) expected of a car 10 and themagnitude (∥{right arrow over (V)}∥) as shown at step 640. Therefore, aselection of how the second threshold (ω_(max)) is set is based onwhether the car 10 is nearly still

$\left( {\omega_{\max} = \frac{\overset{->}{V}}{R_{\min}}} \right)$

or in motion

$\left( {\omega_{\max} = \frac{a_{\max}}{\overset{->}{V}}} \right).$

At step 650, the processor 150 compares a magnitude of the angularrotation rate (∥{right arrow over (ω)}∥)from the gyroscope 130 to thissecond threshold (ω_(max)). If the magnitude of the angular rotationrate (∥ω{right arrow over (ω)}∥) is greater than the second threshold(ω_(max)) the processor 150 may take no interruptive acts and declarethat the cradle has experienced no or insignificant cradle rotation, asshown at step 660. If the magnitude of the angular rotation rate(∥{right arrow over (ω)}∥) is less than the second threshold (ω_(max)),the processor 150 declares that the mobile device 100 has recently beenmoved within the cradle as shown at step 670.

At step 680, the processor 150 may de-weight any results that are basedon measurements from the sensor 110 until the relative orientationbetween the local reference system of the car 10 and the body referencesystem of the mobile device 100 may be reassessed. As an alternative tode-weighting results, the processor 150 may bar the use of sensormeasurements altogether and reset any ongoing computation until therelative position of the mobile device 100 in the cradle or mount may bedetermined again.

The methodologies described herein may be implemented by various meansdepending upon the application. For example, these methodologies may beimplemented in hardware, firmware, software, or any combination thereof.For a hardware implementation, the processing units may be implementedwithin one or more application specific integrated circuits (ASICs),digital signal processors (DSPs), digital signal processing devices(DSPDs), programmable logic devices (PLDs), field programmable gatearrays (FPGAs), processors, controllers, micro-controllers,microprocessors, electronic devices, other electronic units designed toperform the functions described herein, or a combination thereof.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory and executed by a processor unit. Memory may beimplemented within the processor unit or external to the processor unit.As used herein the term “memory” refers to any type of long term, shortterm, volatile, nonvolatile, or other memory and is not to be limited toany particular type of memory or number of memories, or type of mediaupon which memory is stored.

If implemented in firmware and/or software, the functions may be storedas one or more instructions or code on a computer-readable medium.Examples include computer-readable media encoded with a data structureand computer-readable media encoded with a computer program.Computer-readable media includes physical computer storage media. Astorage medium may be any available medium that can be accessed by acomputer. By way of example, and not limitation, such computer-readablemedia can comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium that can be used to store desired program code in the formof instructions or data structures and that can be accessed by acomputer; disk and disc, as used herein, includes compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk andblu-ray disc where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. Combinations of the aboveshould also be included within the scope of computer-readable media.

In addition to storage on computer readable medium, instructions and/ordata may be provided as signals on transmission media included in acommunication apparatus. For example, a communication apparatus mayinclude a transceiver having signals indicative of instructions anddata. The instructions and data are configured to cause one or moreprocessors to implement the functions outlined in the claims. That is,the communication apparatus includes transmission media with signalsindicative of information to perform disclosed functions. At a firsttime, the transmission media included in the communication apparatus mayinclude a first portion of the information to perform the disclosedfunctions, while at a second time the transmission media included in thecommunication apparatus may include a second portion of the informationto perform the disclosed functions.

The previous description of the disclosed aspects is provided to enableany person skilled in the art to make or use the present disclosure.Various modifications to these aspects will be readily apparent to thoseskilled in the art, and the generic principles defined herein may beapplied to other aspects without departing from the spirit or scope ofthe disclosure.

1. A method in a mobile device to detect if the mobile device isunmounted, the method comprising: receiving accelerometer measurements{right arrow over (a)}; determining an average {right arrow over(a)}_(AVG) of the accelerometer measurements {right arrow over (a)};computing a parameter p based on the accelerometer measurements {rightarrow over (a)}; computing a variance σ_(p) ² of the parameter;comparing the variance σ_(p) ² to a threshold; and declaring the mobiledevice is unmounted based on the variance σ_(p) ² being greater than thethreshold.
 2. The method of claim 1, wherein the parameter p comprise anangle θ between the accelerometer measurements {right arrow over (a)}and the average {right arrow over (a)}_(AVG), and the variance σ_(p) ²comprises a variance σ_(θ) ² of the angle θ.
 3. The method of claim 1,wherein the parameter p comprise a magnitude ∥{right arrow over (a)}∥ ofthe accelerometer measurements {right arrow over (a)}, and wherein thevariance σ_(p) ² comprises a variance σ_(∥a∥) ² comprising a variance ofthe magnitude ∥{right arrow over (a)}∥ from a magnitude ∥{right arrowover (a)}_(AVG)∥ of the average {right arrow over (a)}_(AVG).
 4. Amobile device to detect if the mobile device is unmounted, the mobiledevice comprising: an inertial measurement unit comprising anaccelerometer; a processor coupled to the inertial measurement unit; andmemory coupled to the processor, comprising code for: receivingaccelerometer measurements {right arrow over (a)}; determining anaverage {right arrow over (a)}_(AVG) of the accelerometer measurements{right arrow over (a)}; computing a parameter p based on theaccelerometer measurements {right arrow over (a)}; computing a varianceσ_(p) ² of the parameter; comparing the variance σ_(p) ² to a threshold;and declaring the mobile device is unmounted based on the variance σ_(p)² being greater than the threshold.
 5. The mobile device of claim 4,wherein the parameter p comprise an angle θ between the accelerometermeasurements {right arrow over (a)} and the average {right arrow over(a)}_(AVG), and the variance σ_(p) ² comprises a variance σ_(θ) ² of theangle θ.
 6. The mobile device of claim 4, wherein the parameter pcomprise a magnitude ∥{right arrow over (a)}∥ of the accelerometermeasurements {right arrow over (a)}, and wherein the variance σ_(p) ²comprises a variance σ_(∥a∥) ² comprising a variance of the magnitude∥{right arrow over (a)}∥ from a magnitude ∥{right arrow over (a)}_(AVG)∥of the average {right arrow over (a)}_(AVG).
 7. A mobile device todetect if the mobile device is unmounted, the mobile device comprising:means for receiving accelerometer measurements {right arrow over (a)};means for determining an average {right arrow over (a)}_(AVG) of theaccelerometer measurements {right arrow over (a)}; means for computing aparameter p based on the accelerometer measurements {right arrow over(a)}; means for computing a variance σ_(p) ² of the parameter; means forcomparing the variance σ_(p) ² to a threshold; and means for declaringthe mobile device is unmounted based on the variance σ_(p) ² beinggreater than the threshold.
 8. The mobile device of claim 7, wherein theparameter p comprise an angle θ between the accelerometer measurements{right arrow over (a)} and the average {right arrow over (a)}_(AVG), andthe variance σ_(p) ² comprises a variance σ_(θ) ² of the angle θ.
 9. Themobile device of claim 7, wherein the parameter p comprise a magnitude∥{right arrow over (a)}∥ of the accelerometer measurements {right arrowover (a)}, and wherein the variance σ_(p) ² comprises a variance σ_(∥a∥)² comprising a variance of the magnitude ∥{right arrow over (a)}∥ from amagnitude ∥{right arrow over (a)}_(AVG)∥ of the average {right arrowover (a)}_(AVG).
 10. A device comprising a processor and a memorywherein the memory includes software instructions for: receivingaccelerometer measurements {right arrow over (a)}; determining anaverage {right arrow over (a)}_(AVG) of the accelerometer measurements{right arrow over (a)}; computing a parameter p based on theaccelerometer measurements {right arrow over (a)}; computing a varianceσ_(p) ² of the parameter; comparing the variance σ_(p) ² to a threshold;and declaring the mobile device is unmounted based on the variance σ_(p)² being greater than the threshold.
 11. A non-volatile computer-readablestorage medium including program code stored thereon, comprising programcode for: receiving accelerometer measurements {right arrow over (a)};determining an average {right arrow over (a)}_(AVG) of the accelerometermeasurements {right arrow over (a)}; computing a parameter p based onthe accelerometer measurements {right arrow over (a)}; computing avariance σ_(p) ² of the parameter; comparing the variance σ_(p) ² to athreshold; and declaring the mobile device is unmounted based on thevariance σ_(p) ² being greater than the threshold.
 12. A method in amobile device to detect if the mobile device is unmounted, the methodcomprising: receiving gyroscope measurements {right arrow over (ω)};determining an average {right arrow over (ω)}_(AVG) of the gyroscopemeasurements {right arrow over (ω)}; computing a parameter p based onthe gyroscope measurement {right arrow over (ω)}; computing a varianceσ_(p) ² of the parameter; comparing the variance σ_(p) ² to a threshold;and declaring the mobile device is unmounted based on the variance σ_(p)² being greater than the threshold.
 13. The method of claim 12, whereinthe parameter p comprise an angle θ between the gyroscope measurement{right arrow over (a)} and the average {right arrow over (ω)}_(AVG) andthe variance σ_(p) ² comprises a variance σ_(θ) ² of the angle θ. 14.The method of claim 12, wherein the parameter p comprise a magnitude∥{right arrow over (ω)}∥ of the gyroscope measurement {right arrow over(ω)}, and wherein the variance σ_(p) ² a varianceσ_(∥{right arrow over (ω)}∥) ² of the magnitude ∥{right arrow over (ω)}∥from a magnitude ∥{right arrow over (ω)}_(AVG)∥ of the average {rightarrow over (ω)}_(AVG).
 15. A mobile device to detect if the mobiledevice is unmounted, the mobile device comprising: an inertialmeasurement unit comprising a gyroscope; a processor coupled to theinertial measurement unit; and memory coupled to the processor,comprising code for: receiving gyroscope measurements {right arrow over(ω)}; determining an average {right arrow over (ω)}_(AVG) of thegyroscope measurements {right arrow over (ω)}; computing a parameter pbased on the gyroscope measurement {right arrow over (ω)}; computing avariance σ_(p) ² of the parameter; comparing the variance σ_(p) ² to athreshold; and declaring the mobile device is unmounted based on thevariance σ_(p) ² being greater than the threshold.
 16. The mobile deviceof claim 15, wherein the parameter p comprise an angle θ between thegyroscope measurements {right arrow over (ω)} and the average {rightarrow over (ω)}_(AVG), and the variance σ_(p) ² comprises a variance isσ_(ω) ² of the angle θ.
 17. The mobile device of claim 15, wherein theparameter p comprise a magnitude ∥{right arrow over (ω)}∥ of thegyroscope measurements {right arrow over (ω)}, and wherein the varianceσ_(p) ² comprises a variance σ_(∥ω∥) ² comprising a variance of themagnitude ∥{right arrow over (ω)}∥ from a magnitude ∥{right arrow over(ω)}_(AVG)∥ of the average {right arrow over (ω)}_(AVG).
 18. A mobiledevice to detect if the mobile device is unmounted, the mobile devicecomprising: means for receiving gyroscope measurements {right arrow over(ω)}; means for determining an average {right arrow over (ω)}_(AVG) ofthe gyroscope measurements {right arrow over (ω)}; means for computing aparameter p based on the gyroscope measurement {right arrow over (ω)};means for computing a variance σ_(p) ² of the parameter; means forcomparing the variance σ_(p) ² to a threshold; and means for declaringthe mobile device is unmounted based on the variance σ_(p) ² beinggreater than the threshold.
 19. The mobile device of claim 18, whereinthe parameter p comprise an angle θ between the gyroscope measurements{right arrow over (ω)} and the average {right arrow over (ω)}_(AVG), andthe variance σ_(p) ² comprises a variance is σ_(ω) ² of the angle θ. 20.The mobile device of claim 18, wherein the parameter p comprise amagnitude ∥{right arrow over (ω)}∥ of the gyroscope measurements {rightarrow over (ω)}, and wherein the variance σ_(p) ² comprises a varianceσ_(∥ω∥) ² comprising a variance of the magnitude ∥{right arrow over(ω)}∥ from a magnitude ∥{right arrow over (ω)}_(AVG)∥ of the average{right arrow over (ω)}_(AVG).
 21. A device comprising a processor and amemory wherein the memory includes software instructions for: receivinggyroscope measurements {right arrow over (ω)}; determining an average{right arrow over (ω)}_(AVG) of the gyroscope measurements {right arrowover (ω)}; computing a parameter p based on the gyroscope measurement{right arrow over (ω)}; computing a variance σ_(p) ² of the parameter;comparing the variance σ_(p) ² to a threshold; and declaring the mobiledevice is unmounted based on the variance σ_(p) ² being greater than thethreshold.
 22. A non-volatile computer-readable storage medium includingprogram code stored thereon, comprising program code for: receivinggyroscope measurements {right arrow over (ω)}; determining an average{right arrow over (ω)}_(AVG) of the gyroscope measurements {right arrowover (ω)}; computing a parameter p based on the gyroscope measurement{right arrow over (ω)}; computing a variance σ_(p) ² of the parameter;comparing the variance σ_(p) ² to a threshold; and declaring the mobiledevice is unmounted based on the variance σ_(p) ² being greater than thethreshold.
 23. A method in a mobile device to detect if the mobiledevice is unmounted, the method comprising: receiving accelerometermeasurements {right arrow over (a)}; determining an average {right arrowover (a)}_(AVG) of the accelerometer measurements {right arrow over(a)}; computing an angle θ between the average {right arrow over(a)}_(AVG) and an axis perpendicular to a viewable display on the mobiledevice; comparing the angle θ to 90 degrees; and declaring the mobiledevice is unmounted based on the angle θ being less than a thresholdfrom 90 degrees.
 24. A mobile device to detect if the mobile device isunmounted, the mobile device comprising: an inertial measurement unitcomprising an accelerometer; a processor coupled to the inertialmeasurement unit; and memory coupled to the processor, comprising codefor: receiving accelerometer measurements {right arrow over (a)};determining an average {right arrow over (a)}_(AVG) of the accelerometermeasurements {right arrow over (a)}; computing an angle θ between theaverage {right arrow over (a)}_(AVG) and an axis perpendicular to aviewable display on the mobile device; comparing the angle θ to 90degrees; and declaring the mobile device is unmounted based on the angleθ being less than a threshold from 90 degrees.
 25. A mobile device todetect if the mobile device is unmounted, the mobile device comprising:means for receiving accelerometer measurements {right arrow over (a)};means for determining an average {right arrow over (a)}_(AVG) of theaccelerometer measurements {right arrow over (a)}; means for computingan angle θ between the average {right arrow over (a)}_(AVG) and an axisperpendicular to a viewable display on the mobile device; means forcomparing the angle θ to 90 degrees; and means for declaring the mobiledevice is unmounted based on the angle θ being less than a thresholdfrom 90 degrees.
 26. A device comprising a processor and a memorywherein the memory includes software instructions for: receivingaccelerometer measurements {right arrow over (a)}; determining anaverage {right arrow over (a)}_(AVG) of the accelerometer measurements{right arrow over (a)}; computing an angle θ between the average {rightarrow over (a)}_(AVG) and an axis perpendicular to a viewable display onthe mobile device; comparing the angle θ to 90 degrees; and declaringthe mobile device is unmounted based on the angle θ being less than athreshold from 90 degrees.
 27. A non-volatile computer-readable storagemedium including program code stored thereon, comprising program codefor: receiving accelerometer measurements {right arrow over (a)};determining an average {right arrow over (a)}_(AVG) of the accelerometermeasurements {right arrow over (a)}; computing an angle θ between theaverage {right arrow over (a)}_(AVG) and an axis perpendicular to aviewable display on the mobile device; comparing the angle θ to 90degrees; and declaring the mobile device is unmounted based on the angleθ being less than a threshold from 90 degrees.
 28. A method in a mobiledevice to detect if the mobile device is unmounted, the methodcomprising: computing variance values σ_(p) ² for at least one of: (a) asequence of accelerometer measurements ({right arrow over (a)}); (b) asequence of averages ({right arrow over (a)}_(AVG)) of accelerometermeasurements; (c) a sequence of gyroscope measurements ({right arrowover (ω)}); (d) three sequences of scalar channelized gyroscopemeasurements (ω_(X), ω_(Y), ω_(Z)); and (e) a virtual gyroscope headingrate ({dot over ( H); setting a flag if any of the variance values σ_(p)² exceed a respective threshold; and declaring the mobile device is notin a mounted state based on the flag.
 29. The method of claim 28,wherein setting and declaring comprise: counting a number of times anyof the variance values over a window of time exceed a respectivethreshold; and declaring the mobile device is not in a mounted statebased on the number being greater than a threshold.
 30. The method ofclaim 28, wherein computing the variance values for at least one of(a)-(e) comprises computing the variance values for at least two of(a)-(e).
 31. The method of claim 28, wherein computing the variancevalues for at least one of (a)-(e) comprises computing the variancevalues for at least three of (a)-(e).
 32. A mobile device to detect ifthe mobile device is unmounted, the mobile device comprising: aninertial measurement unit comprising an accelerometer; a processorcoupled to the inertial measurement unit; and memory coupled to theprocessor, comprising code for: computing variance values σ_(p) ² for atleast one of: (a) a sequence of accelerometer measurements ({right arrowover (a)}); (b) a sequence of averages ({right arrow over (a)}_(AVG)) ofaccelerometer measurements; (c) a sequence of gyroscope measurements({right arrow over (ω)}); (d) three sequences of scalar channelizedgyroscope measurements (ω_(X), ω_(Y), ω_(Z)); and (e) a virtualgyroscope heading rate ({dot over ( H); setting a flag if any of thevariance values σ_(p) ² exceed a respective threshold; and declaring themobile device is not in a mounted state based on the flag.
 33. A mobiledevice to detect if the mobile device is unmounted, the mobile devicecomprising: means for computing variance values σ_(p) ² for at least oneof: (a) a sequence of accelerometer measurements ({right arrow over(a)}); (b) a sequence of averages ({right arrow over (a)}_(AVG)) ofaccelerometer measurements; (c) a sequence of gyroscope measurements({right arrow over (ω)}); (d) three sequences of scalar channelizedgyroscope measurements (ω_(X), ω_(Y), ω_(Z)); and (e) a virtualgyroscope heading rate ({dot over ( H); means for setting a flag if anyof the variance values σ_(p) ² exceed a respective threshold; and meansfor declaring the mobile device is not in a mounted state based on theflag.
 34. A device comprising a processor and a memory wherein thememory includes software instructions for: computing variance valuesσ_(p) ² for at least one of: (a) a sequence of accelerometermeasurements ({right arrow over (a)}); (b) a sequence of averages({right arrow over (a)}_(AVG)) of accelerometer measurements; (c) asequence of gyroscope measurements ({right arrow over (ω)}); (d) threesequences of scalar channelized gyroscope measurements (ω_(X), ω_(Y),ω_(Z)); and (e) a virtual gyroscope heading rate ({dot over ( H);setting a flag if any of the variance values σ_(p) ² exceed a respectivethreshold; and declaring the mobile device is not in a mounted statebased on the flag.
 35. A non-volatile computer-readable storage mediumincluding program code stored thereon, comprising program code for:computing variance values σ_(p) ² for at least one of: (a) a sequence ofaccelerometer measurements ({right arrow over (a)}); (b) a sequence ofaverages ({right arrow over (a)}_(AVG)) of accelerometer measurements;(c) a sequence of gyroscope measurements ({right arrow over (ω)}); (d)three sequences of scalar channelized gyroscope measurements (ω_(X),ω_(Y), ω_(Z)); and (e) a virtual gyroscope heading rate ({dot over ( H);setting a flag if any of the variance values σ_(p) ² exceed a respectivethreshold; and declaring the mobile device is not in a mounted statebased on the flag.